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Elastic Brings Observability to AI Agents with Amazon Bedrock AgentCore

Developers gain end-to-end monitoring, optimization and troubleshooting for AI Agents deployed with Amazon Bedrock AgentCore

Elastic announced a new integration for Amazon Bedrock AgentCore that brings observability for AI agents and applications directly into the Elasticsearch platform.

The integration allows site reliability engineers (SREs) and developers to monitor, optimize and troubleshoot agents running on Amazon Bedrock AgentCore.

Elastic Observability monitors AI agents and applications by ingesting all logs and metric signals to help you easily see performance, control costs, understand agent reasoning, and keep agent workloads running reliably. This delivers an end-to-end, unified view for deployment with Amazon Bedrock AgentCore and an organization's agents' large language model (LLM) interactions.

“As organizations deploy AI agents to production, the challenge is observing every layer of the agentic AI stack,” said Baha Azarmi, general manager of Elastic Observability. “Elastic’s Agentic AI Observability for Amazon Bedrock AgentCore gives teams curated dashboards to the full LLM execution path, with AI guardrails monitoring for safety and compliance, and predictable AI cost tracking.”

The Elastic Observability AWS Agentcore integration is available now. 

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Elastic Brings Observability to AI Agents with Amazon Bedrock AgentCore

Developers gain end-to-end monitoring, optimization and troubleshooting for AI Agents deployed with Amazon Bedrock AgentCore

Elastic announced a new integration for Amazon Bedrock AgentCore that brings observability for AI agents and applications directly into the Elasticsearch platform.

The integration allows site reliability engineers (SREs) and developers to monitor, optimize and troubleshoot agents running on Amazon Bedrock AgentCore.

Elastic Observability monitors AI agents and applications by ingesting all logs and metric signals to help you easily see performance, control costs, understand agent reasoning, and keep agent workloads running reliably. This delivers an end-to-end, unified view for deployment with Amazon Bedrock AgentCore and an organization's agents' large language model (LLM) interactions.

“As organizations deploy AI agents to production, the challenge is observing every layer of the agentic AI stack,” said Baha Azarmi, general manager of Elastic Observability. “Elastic’s Agentic AI Observability for Amazon Bedrock AgentCore gives teams curated dashboards to the full LLM execution path, with AI guardrails monitoring for safety and compliance, and predictable AI cost tracking.”

The Elastic Observability AWS Agentcore integration is available now. 

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...